What are Type 1 and Type 2 error?

Data Science, AI and ML
, ,
#1

Type 1 and Type 2 error.

#2

Type I error is the rejection of a true null hypothesis (also known as a “false positive” finding or conclusion)

Type II error is the non-rejection of a false null hypothesis (also known as a “false negative” finding or conclusion)

1 Like
#3

In statistical hypothesis testing “type I” and “type II” errors are, respectively, the incorrect rejection of a true null hypothesis and the failure to reject a false null hypothesis.

In other words:

  • A type I error is detecting an effect that is not present.
  • A type II error is failing to detect an effect that is present.